Alternatives
Products that do what My demo for vector embeddings for the Earth's surface does
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2021 · github.com
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2016 · github.com
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2021 · github.com
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2025 · github.com
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2023 · github.com
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As a grad student (and an ADHDer), I had trouble doing literature review systematically. To combat this, I made a website that finds similar papers using the meaning of the thing I am looking for. I used MixedBread's [^1] embedding model to generate vectors from the abstracts. I store and search similar vectors using Milvus [^2] and finally use Gradio [^3] to serve the frontend. I update the vector database weekly by pulling the metadata dataset from Kaggle [^4]. To speed up the search process on my free oracle instance, I binarise the embeddings and use Hamming distance as a metric. I would…
2024 · papermatch.mitanshu.tech
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I made a web app to generate 3D models of real places on earth from land cover and elevation data. Click anywhere on the map to get a polygon, and then click "generate". It should work at most scales, but if your watershed or region selection is too large, the result can be less exciting because it's so flat. As a warning, the 3D models can sometimes be too much for my phone. It's nicer on desktop. I'm still working better on mobile support. The land cover data I'm using gives a cool effect, but at some point I'd like to add in global imagery and clouds. The backend is a Python thread…
2025 · topography.jessekv.com
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Hi HN, I made a web game to practice country locations a while ago and HN liked it, so I thought I'd post my updated version as well. As for how the game works and feels, I'd really recommend you checking it out for yourself, it's free, no signup, no ads. The tech stack is Vue + ts + Tailwind/Daisy for the looks. The learning algorithm is a slight modification of the ts version of FSRS. If you have anything to add, it's open source as well (https://github.com/koljapluemer/learn-worldmap), although not well documented yet. In the end it's just a little sideproject,…
2025 · map.koljapluemer.com
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Discover, evaluate, and access relevant embeddings in your go-to framework. Skip all the infra for scraping, cleaning, indexing, and updating high-quality embeddings.
2023 · embedding.store
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2025 · vectorvfs.readthedocs.io
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I was fascinated by this [0] and this video [1]. After many struggles, I finally built this app that traces images using circles similar to what these videos had shown. The most challenging part (to me) is to find a way to convert images to vector lines. I had tried Potrace, but its output is not suitable for my use case: too many small elements share the same border. Potrace's goal is to represent the original image faithfully using vector lines. But I want to trace the image edges. After searching and trying some Potrace alternatives in vain, I finally found my keyword. Surprisingly (to…
2022 · phqb.github.io
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All commands have the format `output = \func inputs` or just `\function inputs`. Points and scalars are built on the fly. Eg `\line a b` to an empty canvas creates points `a` and `b`, and joins them with a line. One can use broadcasting semantics similar to NumPy and PyTorch in a visual setting (imagine creating a list of circles where one dim corresponds to radius and another to the center). One can also use backpropagation, run gradient descent or visualize vector fields. Almost everything is reactive so changing a variable updates all of the downstream geometry. It also allows anyone to…
May 2026 · tinyvolt.com
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Hey HN, we're excited to show you client-vector-search, a client-side library that helps you embed, store, search, and cache vectors in your browser or node env. We needed it at https://searchbase.app and that's why we've built it. with it you get: 1. easy setup: you only need to add 5 lines of code to build a semantic search 2. no embedding api needed: you don't need an api and have to pay for it unless ure scaling up millions 3. faster search: modern hardware is better than cheap cloud computers (0.5vCPUs) 4. zero latency: no back-and-forth with server-side 5. easy integration…
2023 · clientvectorsearch.com
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Hi HN. Peter here. As a machine learning engineer, I mostly think in terms of feature vectors, embeddings, and matrices. One of the most useful byproducts of deep neural networks is embeddings because they allow us to represent high-dimensional data in terms of lower-dimensional latent vectors. These feature vectors can be used for downstream applications like similarly search, recommendation systems and near duplicate detection. As an ML engineer, I was frustrated by the lack of a datastore in which vectors are first-class citizens. As a result, most ML engineers, including myself, end up…
2021
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2024 · github.com
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Hi HN, I built a little tool to help me get better at remembering country locations on the map: https://learn-worldmap.netlify.app/ It's made from plain HTML/CSS/JS, and not intended to make any money or anything. Just thought that maybe someone else may enjoy it. Also of course happy about any feedback! Cheerz.
2023 · learn-worldmap.netlify.app
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2025 · github.com
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